131 research outputs found
Natural Resources Research Institute Technical Report
No full textProgress Report of the AFOSR AASERT Project : Covering research period 8/1/97 to 7/31/98; Agency No: DOD/F49620-96-1-0330; U of M No: 9606754During the last year, our work has focused on the first three tasks of the project, viz., a) characterization of molecular similarity spaces, b) selection of analogs, and c) similarity based estimation of properties.
In the area of Task 1, the effectiveness of theoretical molecular descriptors vis-avis experimental physicochemical properties in quantifying intermolecular similarity has explored for several sets of compounds with varying physicochemical and biological properties. In Task 2, the various structure spaces developed in Task 1 have been used in the selection of analogs for specific probe compounds. In Task 3, we have used the /c-nearest neighbor (KNN) method to estimate properties of chemicals from various databases. For these experiments, k has been varied from 1-40. The results showed that, for different physicochemical, toxicological and biochemical properties, optimal property estimation is generally obtained in the range of k = 5-10.Natural Resources Research Institute, University of Minnesota DuluthBasak, Subhash C. (1998). Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1997-1998). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187190
Animalia Humorosum: Aesop's animal fables made more believable with a modern twist
No full textThere is much that is unusual about this 8½” square booklet of 28 pages followed by two pages of advertisements for other books by Óla. For starters, the pages are purple with light-colored typeface and cutout colored characters in partial-page illustrations. The T of C uses superscript to indicate page numbers for the twelve fables. That same page clarifies that Ólafia L. Óla is a pseudonym for V. Subhash. In TH, the hare, not the tortoise, challenges to a race upon no provocation. The author turns this tale into the more usual “Rabbit Races the Hedgehog,” famous among Grimm’s fairytales. Every one of the species looks the same to the superficial hare. In LM, after the mouse frees the lion, the hungry lion eats the mouse. “Steer clear of known dangers.” DW is told just as in the tradition. “Better die on your feet than live on your knees.” In TB, the second traveler takes off his socks; the smell of them revolts the bear, who departs. What did the bear whisper to him? “Tell that fellow that trees offer no safety because bears are good climbers.” The ox makes up a snake friend to worry the dog out of his manger. A passing hunter saves the shepherd boy attacked by a real wolf. The mice do manage to get a bell around the cat’s neck by having it ready around their hole when the cat pokes in its head. Two foxes jump for grapes. One reacts according to the tradition. The other says the effort has been stupid. “We are foxes. We don’t eat grapes. Let’s go and catch some rabbits.” One of two crows suggests the traditional pebble approach. The other says that will take too much time and too many pebbles and will dirty the water. He manages to knock over the pitcher and they can drink both from the water spilled and the water still in the overturned pitcher. The owner of the golden goose eventually stops reading his mail, misses paying taxes, loses his property, and has to give up the goose as compensation for the unpaid taxes. The wolf escapes the lambskin and never comes back. The crow removes the doughnut from his mouth and tells the fox to move along.Ólafia L. Óla (V. Subhash
Use of Graph Invariants in Quantitative Structure-Activity Relationship Studies
No full textThis chapter reviews results of research carried out by Basak and collaborators during the past four decades or so in the development of novel mathematical chemodescriptors and their applications in quantitative structure-activity relationship (QSAR) studies related to the prediction of toxicities and bioactivities of chemicals. For chemodescriptors based QSAR studies, we have used graph theoretical, three dimensional (3-D), and quantum chemical indices. The graph theoretic chemodescriptors fall into two major categories: (a) Numerical invariants defined on simple molecular graphs representing only the adjacency and distance relationship of atoms and bonds; such invariants are called topostructural (TS) indices; (b) Topological indices derived from weighted molecular graphs, called topochemical (TC) indices. Collectively, the TS and TC descriptors are known as topological indices (TIs). The set of independent variables used for modeling also includes a group of three-dimensional (3-D) molecular descriptors. Semi-empirical and various levels of ab initio quantum chemical indices have also been used for hierarchical QSAR (HiQSAR) modeling. Results indicate that in many cases of property / activity / toxicity analyzed by us, a TS + TC combination explains most of the variance in the data.
This work is licensed under a Creative Commons Attribution 4.0 International License
Natural Resources Research Institute Technical Report
No full textProgress Report of the AFOSR AASERT Project: Covering research period 8/1/98 to 7/31/99; Agency No: DOD/F49620-96-1-0330; U of M No: 9606754During the third year of the project, our work on the first three tasks of the project; viz., a) characterization of molecular similarity spaces, b) selection of analogs, and c) similarity-based estimation of properties; has continued. However, the focus of our work has shifted to the fourth and final task of the project, viz., the application of neural networks in property estimation.
In the area of Task 1, the effectiveness of theoretical molecular descriptors vis-a- vis experimental physicochemical properties in quantifying intermolecular similarity has been explored for several sets of compounds with varying physicochemical and biological properties. In Task 2, the various structure spaces developed in Task 1 have been used in the selection of analogs for specific probe compounds. In Task 3, we have used the /(-nearest neighbor (KNN) method to estimate properties of chemicals from various databases. For these experiments, k has been varied from 1-40. The results showed that, for different physicochemical, toxicological and biochemical properties, optimal property estimation is generally obtained in the range of k= 5-10. Finally, in Task 4, we have used neural networks for the prediction of toxicological endpoints. In addition, we examined several methods for feature (independent variable) selection using a machine learning techniques, GEFS (genetic ensemble feature selection), based on genetic algorithms. The results show that neural networks, in general, show slight improvement in modeling power over statistical methods, but the use of GEFS to select relevant features for modeling greatly improves the performance of the neural networks.Basak, Subhash C. (1999). Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1998-1999). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187258
Natural Resources Research Institute Technical Report
No full textProgress Report of the AFOSR AASERT Project; Covering research period 8/1/96 to 7/31/97; Agency No: DOD/F49620-96-1-0330;
U of M No:9606754Four classes of theoretical structural parameters, viz., topostructural, topochemical,geometrical and quantum chemical descriptors, have been used in the development of
quantitative structure-activity relationship (QSAR) models for a set of sixty-nine
benzene derivatives. None of the individual classes of parameters was very effective in
predicting toxicity. A hierarchical approach was followed in using a combination of the
four classes of indices in QSAR model development. The results show that the
hierarchical QSAR approach using the algorithmically derived molecular descriptors can estimate the LC50 values of the benzene derivatives reasonably well.Natural Resources Research Institute
University of Minnesota, DuluthBasak, Subhash C; Gute, Brian D. (1997). Quantitative Characterization of Molecular Similarity Spaces: Tools for Computational Toxicology (1996-1997). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187201
Natural Resources Research Institute Technical Report
No full textProgress Report of the Air Force Project; Covering research period 9/1/00 to 8/31/01; Agency No: DOD/F49620-01-1-0098; U of M No： 1613-189-6204i) Attempts have been made to develop quantitative biodescriptors to characterize the effects of chemicals on the proteomics patterns of cells. Applications of biodescriptors to proteomics patterns derived from normal liver cells and cells exposed to peroxisome proliferators showed that quantitative descriptors defined for D/D matrices are capable of rationalizing effects of different types of peroxisome proliferators on the cellular proteome. Such descriptors may find application in predicting the biochemical effects of toxicants from their proteomics patterns.
ii) A novel approach to biodescriptor formulation has been taken, using the concept of partial ordering. The embedded graphs derived thereby have been used to develop new types of invariants for the quantification of proteomics maps.
Biodescriptors developed and reported in the above two manuscripts show reasonable power of discriminating the proteomics patterns resulting from different toxicants. Biodescriptors already developed in this project and those currently under development will provide a battery of such parameters for predictive toxicology. When a certain number of (say n) of such descriptors are calculated for a proteomics map, the map is characterized by a vector consisting of n entries. Elements of such vectors may be used in: a) predicting toxicity using statistical methods such as PCR, PLS, RR or non-linear methods such as neural networks; b) classifying chemicals into various subsets; or c) quantifying the similarity / dissimilarity of toxicants. In light of our earlier study on the prediction of the mode of action (MOAs) of toxicants from chemodescriptors, it is realistic to speculate that the battery of biodescriptors will be useful in predicting the MOAs of toxicants.
Chemodescriptors: i) Chemodescriptors calculated by POLLY and Molconn-Z have been used to predict the cell level toxicity data of halocarbons determined at the Wright Patterson AFB laboratory by Kevin Geiss (unpublished results). The high quality of these models indicate that similar models using calculated chemodescriptors may find application in the prediction of cellular toxicity arising from exposure to pollutants and xenobiotics. ii) In collaboration with Dr. Hawkins, PCR, RR and PLS analyses have been applied-to the prediction of the property / activity / toxicity of various groups of chemicals from their structural descriptors, vis., topostructural indices, topochemical indices, 3-D or shape parameters, and semi-empirical quantum chemical descriptors. This research has resulted in the formulation of robust and useful QSTR methods. iii) Successful quantitative structure-toxicity relationship (QSTR) models have been developed for the exposure assessment of volatile organic chemicals (VOCs), such as halocarbons, using chemodescriptors with the ridge regression statistical technique. These models will be useful in the physiologically-based pharmacokinetic modeling of VOCs. iv) Novel molecular shape descriptors have been developed and applied to predicting properties dependant on molecular shape. These new molecular shape parameters will be useful in QSAR/QSTR studies where molecular shape is a critical determinant of ligand-biotarget interaction in the cellular milieu.
v) New methods have been developed to characterize DNA sequences and their modifications as a result of exposure to toxicants using matrix invariants. Such invariants were defined from newly formulated matrices used to represent macromolecular sequences in a condensed manner. These invariants will be useful tools for research in genomics and bioinformatics.Basak, Subhash C. (2001). Integration of Biodescriptors and Chemodescriptors for Predictive Toxicology: A Mathematical / Computational Approach. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187075
Natural Resources Research Institute Technical Report
No full textProgress Report of the Air Force Project; Covering research period 8/1/98 to 7/31/99; Agency No: DOD/F49620-98-1-0015; U of M No: 1613-189-6158During the past few years we have been involved in the development of new computational methods for quantifying similarity/dissimilarity of chemicals and applications of quantitative molecular similarity analysis (QMSA) techniques in analog selection and property estimation for use in the hazard assessment of chemicals. We have also explored the mathematical nature of the molecular similarity space in order to better understand the basis of analog selection by QMSA methods. The parameter spaces used for QMSA and analog selection were constructed from nonempirical parameters derived from computational chemical graph theory. Occasionally, graph invariants were supplemented with geometrical parameters and quantum chemical indices to study the relative effectiveness of graph invariants vis-a-vis geometrical and quantum chemical parameters in analog selection and property estimation. We carried out comparative studies of nonempirical descriptor spaces and physicochemical property spaces in selecting analogs. Molecular similarity methods were applied in predicting modes of toxic action (MOA) of chemicals. Our similarity/dissimilarity methods have also found successful applications in the discovery of new drug leads by US drug companies.
In this project, we will have four primary goals: 1) development of a hierarchical approach to molecular similarity, 2) formulation of quantitative structure-activity relationship (QSAR) models for predictive toxicology using a hierarchical approach, 3) applications of hierarchical QSAR and QMSA approaches in computational toxicology related to human health and ecological hazard assessment, and 4) the application of hierarchical QMSA and QSAR approaches in estimating potential toxicity of deicing agents.
The first goal of the project is the use of parameters of gradually increasing complexity, viz., topological, topochemical, geometrical, and quantum chemical indices, in the quantification of molecular similarity/dissimilarity of chemicals. We will take a two-tier approach in this area. First, similarity methods will be used in ordering sets of molecules and in selecting structural analogs of toxic chemicals which pose human health and ecological hazards. Secondly, we will use the properties of selected analogs in estimating toxicologically important properties for chemicals. Although different classes of parameters have been used in the characterization of molecular similarity, no systematic study has been carried out in the use of all four classes of parameters, mentioned above, in analog selection and property estimation. We will apply a hierarchical approach to the use of these four types of theoretical molecular descriptors in the quantification of molecular similarity/dissimilarity.
The second goal consists of the development of hierarchical QSAR models for predicting the toxic potential of chemicals using topological and quantum chemical indices. Initially, we will use parameters calculated by semi-empirical methods such as MOP AC and AMP AC. Parameters calculated by ab initio quantum chemical methods will be used in limited cases of QSAR model development, if they are considered necessary.
The third goal of the project will be the prediction of human health hazard and ecotoxicological effects of chemicals using QSAR and QMSA methods developed in the project. Attempts will be made to estimate endpoints, such as, carcinogenicity, mutagenicity, xenoestrogenicity, acute toxicity, transport of chemicals through the blood-brain barrier, biodegradation, and bioconcentration factor.
The fourth goal will involve the utilization of QMSA and QSAR methods developed as part of this project in predicting the potential toxicity of deicing agents.Basak, Subhash C. (1999). Prediction of Health and Environmental Hazards of Chemicals: A Hierarchical Approach using QMSA and QSAR (1998-1999). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187257
Natural Resources Research Institute Technical Report
No full textProgress Report of the Air Force Project; Covering research period 10/1/97 to 7/31/98; Agency No: DOD/F49620-98-1-0015; U of M No: 1613-189-6158During the past few years we have been involved in the development of new computational methods forquantifying similarity/dissimilarity of chemicals and applications of quantitative molecular similarity analysis (QMSA) techniques in analog selection and property estimation for use in the hazard assessment of chemicals. We have also explored the mathematical nature of the molecular similarity space in order to better understand the basis of analog selection by QMSA methods. The parameter spaces used for QMSA and analog selection were constructed from nonempirical parameters derived from computational chemical graph theory. Occasionally, graph invariants were supplemented with geometrical parameters and quantum chemical indices to study the relative effectiveness of graph invariants vis-a-vis geometrical and quantum chemical parameters in analog selection and property estimation. We carried out comparative studies of nonempirical descriptor spaces and physicochemical property spaces in selecting analogs. Molecular similarity methods were applied in predicting modes of toxic action (MOA) of chemicals. Our similarity/dissimilarity methods have also found successful applications in the discovery of new drag leads by US drag companies.
In this project, we will have four primary goals: 1) development of a hierarchical approach to molecular similarity, 2) formulation of quantitative structure-activity relationship (QSAR) models for predictive toxicology using a hierarchical approach, 3) applications of hierarchical QSAR and QMSA approaches in computational toxicology related to human health and ecological hazard assessment, and 4) the application of hierarchical QMSA and QSAR approaches in estimating potential toxicity of deicing agents.
The first goal of the project is the use of parameters of gradually increasing complexity, viz., topological, topochemical, geometrical, and quantum chemical indices, in the quantification of molecular similarity/dissimilarity of chemicals. We will take a two-tier approach in this area. First, similarity methods will be used in ordering sets of molecules and in selecting structural analogs of toxic chemicals which pose human health and ecological hazards. Secondly, we will use the properties of selected analogs in estimating toxicologically important properties for chemicals. Although different classes of parameters have been used in the characterization of molecular similarity, no systematic study has been carried out in the use of all four classes of parameters, mentioned above, in analog selection and property estimation. We will apply a hierarchical approach to the use of these four types of theoretical molecular descriptors in the quantification of molecular similarity/dissimilarity.
The second goal consists of the development of hierarchical QSAR models for predicting the toxic potential of chemicals using topological and quantum chemical indices. Initially, we will use parameters calculated by semi-empirical methods such as MOP AC and AMP AC. Parameters calculated by ab initio quantum chemical methods will be used in limited cases of QSAR model development, if they are considered necessary.
The third goal of the project will be the prediction of human health hazard and ecotoxicological effects of chemicals using QSAR and QMSA methods developed in the project. Attempts will be made to estimate endpoints, such as, carcinogenicity, mutagenicity, xenoestrogenicity, acute toxicity, transport of chemicals through the blood-brain barrier, biodegradation, and bioconcentration factor.
The fourth goal will involve the utilization of QMSA and QSAR methods developed as part of this project in predicting the potential toxicity of deicing agents.Natural Resources Research Institute, University of Minnesota DuluthBasak, Subhash C. (1998). Prediction of Health and Environmental Hazards of Chemical: A Hierarchical Approach Using QMSA and QSAR (1997-1998). Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187189
A novel azetidinyl γ-lactam based peptide with a preference for β-turn conformation
No full textNovel azetidinyl γ-lactam based peptides 1-3 have been synthesized with only compound 1 showing a preference for the β-turn conformation
Natural Resources Research Institute Technical Report
No full textThe principal goal of the cooperative agreement was to develop new molecular
similarity methods and apply them to the risk assessment of environmental chemicals.
To this end, our strategy had the following three-fold objectives:
1. Conduct an International workshop on " MOLECULAR SIMILARITY IN RISK ASSESSMENT " where internationally known experts in toxicology, computational chemistry, mathematical chemistry, structure-activity relationships and risk assessment of chemicals were brought together. These experts provided their opinions about how MOLECULAR SIMILARITY methods should be developed and used for risk assessment of chemicals. Ten experts were interviewed by the NRRI team and subsequently their input was summarized in a technical report submitted to USEPA. The workshop was part of QSAR ’92, an international conference held in Duluth, Minnesota jointly by the Natural Resources Research Institute-University of Minnesota and U S Environmental Protection Agency. The experts also submitted written manuscripts as part of the workshop. The workshop report reflected on different aspects of hazard assessment and molecular similarity.
2. Develop molecular similarity methodology by incorporating the inputs of the experts mentioned in item 1 above, along with our expertise in these methods. Special attention was given to the use of NONEMPIRICAL PARAMETERS (e.g., values calculated directly from the chemical structure) as opposed to empirical (or experimental) parameters because most chemicals used in the environment do not have experimental data necessary for detailed hazard assessment.
3. Apply the computational molecular similarity methods developed during the project in the selection of analogs and in estimation of environmentally important properties of these chemicals.Basak, Subhash C; Niemi, Gerald J; Host, George E. (1995). Computational Techniques to Quantify Chemical Similarity: Tools for Risk Assessment. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/187241
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